Strukturalarning

Strukturalarning is the process of inferring the structure of a model or representation from data. It focuses on discovering how variables relate to each other, beyond estimating fixed parameters. The term appears in multiple languages as a variant of "structure learning" and is used across statistics, machine learning, and artificial intelligence.

In many contexts, strukturalarning aims to recover a graph that encodes conditional independencies or causal relationships among variables. Graphical models such as Bayesian networks and Markov networks are common targets, though the concept also covers causal structure discovery and network topology in other formalisms.

Methods are typically categorized into constraint-based approaches, score-based approaches, and hybrids. Constraint-based methods test conditional independencies to prune edges, with algorithms like PC being among the most cited. Score-based methods search over graph structures using a scoring function such as BIC or MDL, often with heuristic optimization.

A related subfield is neural architecture search, where the structure of a neural network is learned or optimized. The field faces challenges including combinatorial search complexity, identifiability from observational data, and sensitivity to data quality. Robust evaluation requires carefully designed benchmarks and consideration of equivalent structures that explain the data similarly.

Applications span genomics, systems biology, finance, social science, and beyond, including automated model selection and causal discovery. The term strukturalarning is sometimes used interchangeably with structure learning, and regional language usage may prefer translations such as "pembelajaran struktur" or "struktur pembelajaran."